{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os\n",
    "\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "from alibi_detect.od import SpectralResidual\n",
    "\n",
    "import seaborn as sns\n",
    "sns.set(rc={'figure.figsize':(16,8)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading dcos_docker.csv\n",
      "Reading os_linux.csv\n",
      "Reading db_oracle_11g.csv\n",
      "Reading mw_redis.csv\n",
      "Reading dcos_container.csv\n"
     ]
    }
   ],
   "source": [
    "data_path = '../../../../data/train_data/host'\n",
    "dfs = {}\n",
    "for file in os.listdir(data_path):\n",
    "    print('Reading ' + file)\n",
    "    dfs[file[:-4]] = pd.read_csv(data_path+'/'+file) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['docker_008' 'docker_003' 'docker_002' 'docker_005' 'docker_004'\n",
      " 'docker_007' 'docker_001' 'docker_006']\n",
      "['container_session_used' 'container_fgct' 'container_cpu_used'\n",
      " 'container_thread_idle' 'container_thread_running'\n",
      " 'container_thread_total' 'container_fgc' 'container_thread_used_pct'\n",
      " 'container_mem_used']\n"
     ]
    }
   ],
   "source": [
    "print(dfs['dcos_docker']['cmdb_id'].unique())\n",
    "print(dfs['dcos_docker']['name'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-53-d75d08c3f58e>:2: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  df = df[df.name == 'CPU_user_time'][df.cmdb_id=='os_001']\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1444\n",
      "1\n",
      "[0.67467  0.516088 0.428563 0.446497 0.421583 0.438188 0.400439 0.399082\n",
      " 0.354432 0.440988 0.592407 0.868258 0.407601 0.427297 0.417635 0.431346\n",
      " 0.407357 0.425947 0.392397 0.435218 0.394801 0.659414 0.382673 0.378203\n",
      " 0.464185 0.393334 0.397764 0.430963 0.410509 0.436827 0.408742 0.65405\n",
      " 0.406159 0.453187 0.407732 0.44916  0.396252 0.401835 0.401835 0.404374\n",
      " 0.43823  0.687467 0.41702  0.411397 0.396219 0.426733 0.417281 0.415457\n",
      " 0.425248 0.446106 0.446106 0.633738 0.455815 0.401768 0.40885 ]\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "Mixing dicts with non-Series may lead to ambiguous ordering.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-53-d75d08c3f58e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     45\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcatenate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'value'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mlb\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mub\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'value'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mlb\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mub\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     46\u001b[0m \u001b[0minst_s\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mod\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mreturn_instance_score\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'data'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 47\u001b[0;31m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlineplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minst_s\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minst_s\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     48\u001b[0m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlineplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     49\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minst_s\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/ml/lib/python3.8/site-packages/seaborn/_decorators.py\u001b[0m in \u001b[0;36minner_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     44\u001b[0m             )\n\u001b[1;32m     45\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 46\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     47\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     48\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/ml/lib/python3.8/site-packages/seaborn/relational.py\u001b[0m in \u001b[0;36mlineplot\u001b[0;34m(x, y, hue, size, style, data, palette, hue_order, hue_norm, sizes, size_order, size_norm, dashes, markers, style_order, units, estimator, ci, n_boot, seed, sort, err_style, err_kws, legend, ax, **kwargs)\u001b[0m\n\u001b[1;32m    676\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    677\u001b[0m     \u001b[0mvariables\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_LinePlotter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_semantics\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlocals\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 678\u001b[0;31m     p = _LinePlotter(\n\u001b[0m\u001b[1;32m    679\u001b[0m         \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvariables\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvariables\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    680\u001b[0m         \u001b[0mestimator\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mci\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mci\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_boot\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mn_boot\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mseed\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mseed\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/ml/lib/python3.8/site-packages/seaborn/relational.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data, variables, estimator, ci, n_boot, seed, sort, err_style, err_kws, legend)\u001b[0m\n\u001b[1;32m    365\u001b[0m         )\n\u001b[1;32m    366\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 367\u001b[0;31m         \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvariables\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvariables\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    368\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    369\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mestimator\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mestimator\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/ml/lib/python3.8/site-packages/seaborn/_core.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data, variables)\u001b[0m\n\u001b[1;32m    602\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvariables\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    603\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 604\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0massign_variables\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvariables\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    605\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    606\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mvar\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcls\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_semantic_mappings\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/ml/lib/python3.8/site-packages/seaborn/_core.py\u001b[0m in \u001b[0;36massign_variables\u001b[0;34m(self, data, variables)\u001b[0m\n\u001b[1;32m    665\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    666\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minput_format\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"long\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 667\u001b[0;31m             plot_data, variables = self._assign_variables_longform(\n\u001b[0m\u001b[1;32m    668\u001b[0m                 \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mvariables\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    669\u001b[0m             )\n",
      "\u001b[0;32m~/anaconda3/envs/ml/lib/python3.8/site-packages/seaborn/_core.py\u001b[0m in \u001b[0;36m_assign_variables_longform\u001b[0;34m(self, data, **kwargs)\u001b[0m\n\u001b[1;32m    909\u001b[0m         \u001b[0;31m# Construct a tidy plot DataFrame. This will convert a number of\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    910\u001b[0m         \u001b[0;31m# types automatically, aligning on index in case of pandas objects\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 911\u001b[0;31m         \u001b[0mplot_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mplot_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    912\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    913\u001b[0m         \u001b[0;31m# Reduce the variables dictionary to fields with valid data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/ml/lib/python3.8/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data, index, columns, dtype, copy)\u001b[0m\n\u001b[1;32m    466\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    467\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 468\u001b[0;31m             \u001b[0mmgr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minit_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    469\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMaskedArray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    470\u001b[0m             \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmrecords\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mmrecords\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/ml/lib/python3.8/site-packages/pandas/core/internals/construction.py\u001b[0m in \u001b[0;36minit_dict\u001b[0;34m(data, index, columns, dtype)\u001b[0m\n\u001b[1;32m    281\u001b[0m             \u001b[0marr\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mis_datetime64tz_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marr\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0marr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0marr\u001b[0m \u001b[0;32min\u001b[0m \u001b[0marrays\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    282\u001b[0m         ]\n\u001b[0;32m--> 283\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0marrays_to_mgr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marrays\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata_names\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    284\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    285\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/ml/lib/python3.8/site-packages/pandas/core/internals/construction.py\u001b[0m in \u001b[0;36marrays_to_mgr\u001b[0;34m(arrays, arr_names, index, columns, dtype, verify_integrity)\u001b[0m\n\u001b[1;32m     76\u001b[0m         \u001b[0;31m# figure out the index, if necessary\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     77\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mindex\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 78\u001b[0;31m             \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mextract_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marrays\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     79\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     80\u001b[0m             \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mensure_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/ml/lib/python3.8/site-packages/pandas/core/internals/construction.py\u001b[0m in \u001b[0;36mextract_index\u001b[0;34m(data)\u001b[0m\n\u001b[1;32m    398\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    399\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mhave_dicts\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 400\u001b[0;31m                 raise ValueError(\n\u001b[0m\u001b[1;32m    401\u001b[0m                     \u001b[0;34m\"Mixing dicts with non-Series may lead to ambiguous ordering.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    402\u001b[0m                 )\n",
      "\u001b[0;31mValueError\u001b[0m: Mixing dicts with non-Series may lead to ambiguous ordering."
     ]
    }
   ],
   "source": [
    "df = dfs['os_linux']\n",
    "df = df[df.name == 'CPU_user_time'][df.cmdb_id=='os_001']\n",
    "\n",
    "# df = dfs['dcos_docker']\n",
    "# df = df[df.name == 'container_cpu_used'][df.cmdb_id=='docker_003']\n",
    "def get_past(df, curr_time, window_size):\n",
    "    step_size = 1000*60\n",
    "    df_new = pd.DataFrame(columns=['timestamp', 'value'])\n",
    "\n",
    "    for time in [curr_time - step_size*x for x in range(window_size)][::-1]:\n",
    "        if len(df[df.timestamp == time]) == 0:\n",
    "            plug_time = np.max(df[df.timestamp < time]['timestamp'])\n",
    "            row = {}\n",
    "            row['timestamp'] = time\n",
    "            row['value'] = df.loc[df.timestamp==plug_time, 'value'].values[0]\n",
    "            df_new = df_new.append(row, ignore_index=True)\n",
    "        else:\n",
    "            row = {}\n",
    "            row['timestamp'] = time\n",
    "            row['value'] = df.loc[df.timestamp==time, 'value'].values[0]\n",
    "            df_new = df_new.append(row, ignore_index=True)\n",
    "    for time in [curr_time + step_size*x for x in range(5)]:\n",
    "        row = {}\n",
    "        row['timestamp'] = time\n",
    "        value = np.mean(df_new['value'].values)\n",
    "        df_new = df_new.append(row, ignore_index=True)\n",
    "    print(len(df_new))\n",
    "    return df_new\n",
    "\n",
    "df = get_past(df, np.max(df.timestamp.unique()), len(df))\n",
    "\n",
    "od = SpectralResidual(\n",
    "    threshold=1,\n",
    "    window_amp=40,\n",
    "    window_local=40,\n",
    "    n_est_points=1,\n",
    "    n_grad_points=1\n",
    ")\n",
    "\n",
    "# od.infer_threshold(df['value'].values, threshold_perc=99.7)\n",
    "lb = -60\n",
    "ub = -5\n",
    "print(od.threshold)\n",
    "print(df['value'].values[lb:ub])\n",
    "data = np.concatenate([df['value'].values[lb:ub], df['value'].values[lb:ub]])\n",
    "inst_s = od.predict(data,return_instance_score=True)['data']\n",
    "sns.lineplot(x=range(len(inst_s)),y=inst_s)\n",
    "sns.lineplot(x=range(len(data)),y=data)\n",
    "print(inst_s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'data': {'instance_score': array([ 6.53679279e-01,  5.09057603e-01,  3.83271947e-01,  5.78411194e-01,\n",
      "        4.85190802e-01,  3.99414445e-01,  1.42947273e+00,  1.87761030e-01,\n",
      "        1.64056607e-01,  1.60338590e-01, -8.18464646e-03,  1.79537919e-01,\n",
      "       -2.21522549e-02,  4.02268293e-02,  1.37016406e-01,  1.26743877e-01,\n",
      "        7.27532529e-01, -8.19111725e-02, -1.89786879e-02, -7.92808474e-02,\n",
      "       -1.93762283e-01, -2.31544107e-01, -3.05891056e-01, -1.55762534e-01,\n",
      "       -5.71569457e-02, -1.34635773e-01, -3.27252626e-01,  5.85631216e-01,\n",
      "       -1.40155344e-01, -2.28545521e-02, -1.29803797e-01, -1.76294347e-01,\n",
      "        8.45315505e-01,  8.04273158e-01, -2.59913915e-02, -1.85918776e-01,\n",
      "        3.94344102e-01, -1.09189669e-01, -1.47347502e-01, -1.84439133e-01,\n",
      "       -6.48696867e-02, -1.38342293e-03, -1.51178518e-01, -1.49111111e-01,\n",
      "       -3.24559776e-03, -5.91845398e-02,  4.61210187e-01, -1.96857236e-01,\n",
      "       -1.54623643e-01, -1.17099821e-01, -5.58689422e-02, -1.32589985e-01,\n",
      "       -1.60401615e-01,  1.19376268e-02,  2.18725938e-02, -5.65404551e-02,\n",
      "        5.60274249e-01, -4.52517495e-02, -3.60536104e-02,  2.11585654e-01,\n",
      "       -2.97088494e-02, -5.02829122e-02, -1.10161411e-01, -7.46507755e-02,\n",
      "       -7.74935280e-02, -5.11109122e-02,  4.58900284e-01, -1.13867996e-01,\n",
      "       -5.56128512e-02, -1.02756958e-01, -2.13039728e-01, -1.57466544e-01,\n",
      "       -8.76888541e-02, -1.61846523e-01, -1.51933404e-01, -2.64441891e-02,\n",
      "        5.86171637e-01,  7.73676543e-01, -1.51893553e-01, -1.50517160e-01,\n",
      "       -1.00555433e-01, -1.93420108e-01, -1.35164030e-01, -2.86030187e-02,\n",
      "       -1.64150512e-02,  1.60184161e-02,  5.68148692e-01, -1.44680999e-01,\n",
      "       -9.49653993e-03, -1.21775819e-01,  2.47237018e-01,  1.95173140e-01,\n",
      "       -7.89184748e-02, -6.59879415e-02, -1.52446517e-01, -6.77784480e-02,\n",
      "       -1.23229527e-01, -1.57608140e-01,  2.95504315e-02, -8.30246717e-02,\n",
      "       -9.16539693e-02, -8.05237870e-02, -5.60581140e-02, -1.54813096e-01,\n",
      "       -7.75711570e-03, -4.06614358e-02,  3.39259550e-02,  3.41881622e-01,\n",
      "       -1.09042080e-01,  8.74398089e-01, -1.28411936e-01, -1.72643911e-01,\n",
      "       -9.30424313e-02, -1.07689547e-01, -1.27993304e-01, -1.47519861e-01,\n",
      "        1.66072491e-02,  5.05765427e-01,  4.28427523e-03,  1.22406436e-01,\n",
      "       -6.51372079e-02, -5.20118183e-02, -5.57060635e-02, -1.57144528e-01,\n",
      "       -1.11304900e-01, -1.03928471e-02, -6.60578974e-02,  4.53443880e-01,\n",
      "        1.06483506e-01, -1.37132934e-01, -1.39456133e-01, -1.32214943e-01,\n",
      "       -1.23456997e-01, -9.34507914e-02, -1.03008363e-01,  1.38213339e-01,\n",
      "       -1.53610197e-01,  4.39263805e-01, -1.23761229e-01, -2.38407036e-01,\n",
      "       -8.48236814e-02, -1.09955237e-01, -6.75956933e-02, -5.82852410e-02,\n",
      "       -1.12282146e-01, -1.19023837e-01,  2.63941549e-02,  3.36252325e-01,\n",
      "        2.32093019e-02,  9.09048048e-01,  1.65767962e-01,  1.35488265e-01,\n",
      "       -9.75996099e-02, -2.07720496e-01, -1.22665660e-01, -1.79886388e-01,\n",
      "       -5.77327168e-02,  4.54795712e-01, -7.18584478e-05, -2.59132445e-01,\n",
      "       -1.90682889e-01, -1.90343701e-01, -5.52220315e-02, -7.33602506e-02,\n",
      "       -1.26620572e-01,  1.95433712e-01, -8.87590898e-02,  5.67872231e-01,\n",
      "       -8.13862303e-02, -1.75835309e-01, -4.60884788e-02, -5.64112485e-02,\n",
      "       -1.03306044e-01, -1.87075654e-02, -1.39040668e-01, -9.71956270e-02,\n",
      "       -9.88690870e-02,  6.21203063e-01, -3.87512082e-02,  1.84526446e-01,\n",
      "       -1.52637079e-01, -7.57291521e-02, -1.10936461e-01, -1.89651887e-01,\n",
      "       -2.25503618e-01, -1.17189394e-01, -1.43606412e-02,  5.00299398e-01,\n",
      "       -3.36401843e-02, -7.51887147e-02, -2.23797856e-01, -2.87683906e-01,\n",
      "       -2.12687352e-01, -1.25671877e-01,  1.22235337e+00, -1.96216182e-01,\n",
      "       -4.05026505e-02,  5.06087670e-01, -7.63704889e-02, -9.43315288e-02,\n",
      "       -4.95063184e-02, -5.30352557e-02, -3.69332766e-02,  1.13321229e-01,\n",
      "       -3.31712438e-01, -1.95992112e-01, -1.03374287e-01,  4.74333643e-01,\n",
      "        6.96705067e-02,  1.35766437e-01, -6.23102836e-02, -2.30935046e-01,\n",
      "       -2.07165832e-01, -1.85846272e-01, -7.83785355e-02,  2.03331088e-02,\n",
      "       -1.10477840e-02,  5.56289603e-01, -4.11571103e-02, -5.77437695e-02,\n",
      "       -1.00919143e-01, -4.95768094e-02,  9.02809841e-03,  1.56970071e-02,\n",
      "       -2.32293854e-02, -4.56647189e-02, -7.35434392e-02,  3.79470055e-01,\n",
      "       -1.36090734e-01, -9.87097338e-02, -1.40678670e-01, -1.24932738e-01,\n",
      "       -8.78574966e-02, -1.12846854e-01, -1.06780578e-01, -6.23438548e-02,\n",
      "       -9.22560648e-02,  4.54286027e-01,  6.17394012e-01, -5.53858469e-02,\n",
      "       -1.15664103e-01, -1.35810047e-01, -3.74630153e-02, -1.26290460e-01,\n",
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      "       -1.16030207e-01, -1.64967875e-01, -2.84734945e-02, -1.70419424e-01,\n",
      "       -1.31232359e-01, -4.84669153e-02, -1.14006579e-01,  3.32064342e-02,\n",
      "        1.43098970e+00, -8.89994082e-03,  2.05348972e-01, -1.92144914e-01,\n",
      "       -2.47901857e-01, -1.73457171e-01, -2.17128854e-01, -3.55987416e-01,\n",
      "       -2.99455588e-01, -5.45724030e-02,  4.24204540e-01, -7.01778088e-02,\n",
      "       -2.04421438e-01, -2.00888824e-01, -1.60425160e-01, -1.22937738e-01,\n",
      "       -1.89032154e-01,  1.17484634e+00,  1.18829265e+00, -5.85294924e-02,\n",
      "        2.90841106e-01, -1.74385984e-01, -8.54952841e-02, -3.39036373e-01,\n",
      "       -1.74010150e-01, -2.00147230e-01, -7.49794535e-02, -3.26203003e-01,\n",
      "       -2.32697185e-01, -1.09770273e-01, -2.36528528e-01,  7.40318063e-01,\n",
      "       -1.16638179e-01,  1.40389757e-01, -8.88355159e-02, -1.87785823e-01,\n",
      "        6.41737800e-02, -4.25824846e-02, -9.50497070e-02,  2.96317529e-02,\n",
      "        5.87677298e-01, -6.10144172e-02, -5.68656491e-02, -2.22661303e-01,\n",
      "       -2.35087296e-01, -8.72961870e-02, -2.11686072e-01, -1.76517443e-01,\n",
      "        2.90220166e-01, -9.22143585e-02,  5.87251057e-01, -7.61170959e-02,\n",
      "       -2.97957361e-02, -3.08180052e-02, -4.78584659e-02, -6.88115830e-02,\n",
      "       -1.14183870e-01, -1.45405502e-01, -1.87079401e-01, -8.08389531e-02,\n",
      "        5.48072404e-01,  1.86085306e-01,  2.23714757e-01,  2.98369618e-02,\n",
      "       -4.51296745e-02, -1.66003051e-01, -1.69821744e-01,  7.28728812e-02,\n",
      "       -2.07914283e-01, -1.07754474e-01,  5.25585734e-01, -4.55752422e-02,\n",
      "       -9.60145046e-02, -9.59002345e-04, -1.89288013e-01, -1.45055611e-01,\n",
      "        9.33333280e-02,  3.86848370e-02,  2.49749440e-01,  2.93402017e-01,\n",
      "        1.04034668e+00,  2.64566684e-01,  3.02115373e-01,  4.18482208e-01,\n",
      "        6.29992789e-01,  6.52428384e-01,  8.23381212e-01,  8.56042130e-01]), 'feature_score': None, 'is_outlier': array([0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0])}, 'meta': {'name': 'SpectralResidual', 'detector_type': 'online', 'data_type': 'time-series'}}\n"
     ]
    }
   ],
   "source": [
    "    print( od.predict(df['value'].values[:window_size])['data']['is_outlier'][-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-8-d39ce934a421>:2: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  df = df[df.name == 'CPU_user_time'][df.cmdb_id=='os_001']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='timestamp', ylabel='value'>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = dfs['os_linux']\n",
    "df = df[df.name == 'CPU_user_time'][df.cmdb_id=='os_001']\n",
    "sns.lineplot(y='value',x='timestamp',data=df)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
